The Relationship between Repeated Sprint Performance and Velocity Values during Loaded-Squat Jump Exercise
Bibliographic record
Abstract
The purpose of this study is to investigate the relationship between repeated sprint performance and velocity values during loaded-squat jump exercise. In accordance with this purpose, 23 kickboxing athletes (age: 21,1 ± 2,10 years; height: 178,7±5,01 cm; weight: 70,8±7,85 kg) participated voluntarily in this study. Participants were performed to repeated sprint test and loaded-squat jump exercise by using an external load corresponding to 40 % of their body weight and velocity parameters were obtained as mean velocity (MV), mean propulsive velocity (MPV) and peak velocity (PV). Descriptive statistics were used in analysis of data. In addition, Spearman Correlation Analysis was used to determine the relationship between repeated sprint performance and velocity values during loaded-squat jump exercise. According to the analysis results; while total running times and fatigue index values in repeated sprint test of subjects were obtained as 52.4 (±1.96 sec) and 4.98 (±2.36%) respectively; velocity values during loaded-squat jump exercise were obtained as 1.38 (±,07 m/s-1) for MP, 1.54 (±,10 m/s-1) for MPV, and 2.58 (±,15 m/s-1) for PV. In addition, it was determined that there was no any statistically significant difference between MP (r= - ,148; r= - ,072, p>0.05, respectively), MPV (r= - ,335; r= - ,150, p>0.05, respectively), PV (r= ,010; r= - ,034, p>0.05 respectively) and total running time and fatigue index values in repeated sprint test. As a result, it can be suggested that velocity values during loaded squat jump exercise have no statistically significant effect on repeated sprint performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".